Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

Frank Neumann, Carsten Witt

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials explains the most important results achieved in this area.

    The presenters show how runtime behavior can be analyzed in a rigorous way, in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems.

    The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com.
    Original languageEnglish
    Title of host publicationProceedings of the fourteenth international conference on Genetic and evolutionary computation : Companion
    PublisherAssociation for Computing Machinery
    Publication date2012
    Pages1035-1058
    ISBN (Print)978-1-4503-1178-6
    DOIs
    Publication statusPublished - 2012
    EventGenetic and Evolutionary Computation Conference (GECCO 2012) - Philadelphia, United States
    Duration: 7 Jul 201211 Jul 2012
    http://www.sigevo.org/gecco-2012/

    Conference

    ConferenceGenetic and Evolutionary Computation Conference (GECCO 2012)
    CountryUnited States
    CityPhiladelphia
    Period07/07/201211/07/2012
    Internet address

    Cite this

    Neumann, F., & Witt, C. (2012). Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation: Companion (pp. 1035-1058). Association for Computing Machinery. https://doi.org/10.1145/2330784.2330928